AI & Data Security Engineer

Apple•Austin, TX

About The Position

Apple's US Decision Intelligence (DI) team is looking for a talented individual who is passionate about crafting, implementing, and operating AI solutions that have a direct and measurable impact on Apple Sales and its customers. We're looking for an AI & Data Security Engineer responsible for securing data across the full AI lifecycle, from data classification and enforcement of access controls to model deployment and agentic applications. This role designs and enforces row-level security policies, API-driven access controls, and role-based data grants across AI pipelines, chat interfaces, and autonomous agents. Partners closely with Data Governance, Legal, and Engineering to align AI data usage with enterprise policy and regulatory requirements. Leads red team exercises to proactively identify vulnerabilities in AI systems and drives remedial actions. Owns the development of security standards and guidelines that enable product teams to build AI applications securely by default, at scale.

Requirements

8+ years of professional experience in data security, cybersecurity, security architecture, or data engineering with a primary focus on security.
Data Platform Security: Proven hands-on experience designing and implementing Role-Based Access Control (RBAC), row-level, and column-level security policies in modern cloud data platforms (specifically Snowflake and/or Databricks/DBX).
API & Application Security: Strong expertise in API security controls, authentication, and authorization protocols (e.g., OAuth2, OIDC, SAML, JWT) to protect data access.
Programming Skills: Proficiency in Python, Java, Go, or similar languages used for scripting, automation, and building security controls within data pipelines.
Compliance & Privacy: Solid understanding of data privacy regulations (e.g., GDPR, CCPA) and experience translating these regulatory requirements into technical data governance and access controls.
Monitoring & Auditing: Experience implementing security logging, audit trails, and monitoring solutions to detect unauthorized access or data exfiltration.
Education: Bachelor"s degree in Computer Science, Cybersecurity, Information Systems, or equivalent practical experience.
AI/ML Security Expertise: Direct experience securing AI/ML lifecycles, LLM-powered applications, or autonomous AI agents (e.g., securing RAG architectures, mitigating prompt injection, defining data access boundaries for AI).
Adversarial Testing: Experience leading or participating in red team exercises, penetration testing, or threat modeling specifically tailored to machine learning models and AI systems.
Cross-Functional Leadership: Demonstrated ability to partner effectively with non-technical stakeholders, including Legal, Privacy, and Data Governance teams, to establish and enforce enterprise wide security standards.
Advanced Threat Detection: Experience building or deploying anomaly detection systems to identify malicious activity within complex data pipelines.
Communication Skills: Strong technical writing skills with a track record of creating developer guidelines, security standards, and best practices that enable secure-by-default engineering at scale.

Nice To Haves

Master"s degree in a relevant field, or industry recognized security certifications (e.g., CISSP, CISM, Cloud Security certifications).

Responsibilities

Design and implement security architecture for AI use cases, ensuring secure data access and usage through role-based access controls and authorized provisioning.
Ensure AI use cases are aligned with Apple’s data classification standards, including appropriate data handling, storage, retention requirements and access controls.
Implement and manage user id and persona based row-level security policies for data stored in Snowflake and other data systems connected to US applications.
Implement and maintain row-level security policies based on user identity and persona across DBX and other data platforms supporting U.S. applications.
Design and implement API-based security controls for AI applications, including authentication, authorization and data access policies to protect sensitive information and ensure compliant data consumption.
Lead adversarial testing of AI systems to identify vulnerabilities, drive remediation, and safeguard Apple data from misuse and malicious activity.
Define and enforce data access boundaries for AI agents, governing permitted data sources, actions and restricting sensitive data access.
Define and enforce data access policies for LLM-powered chat applications, governing usage of structured and unstructured data sources, documents and context that may be surfaced in agentic responses.
Partner with Data Governance, Legal, Privacy and Engineering teams to ensure AI data usage complies with enterprise policies, regulatory requirements (e.g., GDPR, CCPA), and internal data governance standards.
Monitor & Audit AI data access pipelines through logging, anomaly detection and audit trails to detect unauthorized access, data exfiltration attempts or policy violations.
Define and enforce US-wide AI data security standards, best practices, and developer guidelines to implement role-based access controls, enabling secure-by-default data practices at scale.